2013
DOI: 10.1103/physrevlett.110.165507
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Quantitative Prediction of Effective Toughness at Random Heterogeneous Interfaces

Abstract: The propagation of an adhesive crack through an anisotropic heterogeneous interface is considered. Tuning the local toughness distribution function and spatial correlation is numerically shown to induce a transition between weak to strong pinning conditions. While the macroscopic effective toughness is given by the mean local toughness in case of weak pinning, a systematic toughness enhancement is observed for strong pinning (the critical point of the depinning transition). A selfconsistent approximation is sh… Show more

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Cited by 47 publications
(47 citation statements)
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“…Fig.7-B shows the PDF of E dep for experiment I at strain S b when an avalanche is triggered. This energy is sometimes called the depinning energy, and in the framework of the pinning-depinning theory, several models predict Gaussian statistics [47][48][49][50]. In our case, unlike these models, we observe a power-law distribution, and the measured experimental exponent is close to 1: γ = −1.08 ± 0.1.…”
Section: A Global Avalanchessupporting
confidence: 40%
“…Fig.7-B shows the PDF of E dep for experiment I at strain S b when an avalanche is triggered. This energy is sometimes called the depinning energy, and in the framework of the pinning-depinning theory, several models predict Gaussian statistics [47][48][49][50]. In our case, unlike these models, we observe a power-law distribution, and the measured experimental exponent is close to 1: γ = −1.08 ± 0.1.…”
Section: A Global Avalanchessupporting
confidence: 40%
“…Besides, the results we have presented are complementary to previous studies of elastic interfaces in random media with long-range elasticity [66,67]. We can define the ratio Ξ 0 = D 1/2 /c, which allows to rewrite the finite-size coordinate of the phase diagram ( Fig.…”
Section: Discussionsupporting
confidence: 67%
“…In the latter case, out-of-equilibrium mechanisms may allow the effective property to reach values above the Voigt bound [13,14].…”
Section: Figmentioning
confidence: 99%